ajrayman/Orderliness_binary

1---2library_name: transformers3license: mit4base_model: microsoft/deberta-v3-base5tags:6- generated_from_trainer7metrics:8- accuracy9- precision10- recall11- f112model-index:13- name: Orderliness_binary14  results: []15---16 17<!-- This model card has been generated automatically according to the information the Trainer had access to. You18should probably proofread and complete it, then remove this comment. -->19 20# Orderliness_binary21 22This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.23It achieves the following results on the evaluation set:24- Loss: 0.906525- Accuracy: 0.646326- Precision: 0.627527- Recall: 0.718228- F1: 0.669829- Auc: 0.694930 31## Model description32 33More information needed34 35## Intended uses & limitations36 37More information needed38 39## Training and evaluation data40 41More information needed42 43## Training procedure44 45### Training hyperparameters46 47The following hyperparameters were used during training:48- learning_rate: 2e-0549- train_batch_size: 3250- eval_batch_size: 3251- seed: 123452- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0853- lr_scheduler_type: linear54- lr_scheduler_warmup_ratio: 0.0655- num_epochs: 856 57### Training results58 59| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Auc    |60|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|61| No log        | 1.0   | 118  | 0.6405          | 0.6413   | 0.6119    | 0.7706 | 0.6821 | 0.6834 |62| No log        | 2.0   | 236  | 0.6950          | 0.5890   | 0.5515    | 0.9476 | 0.6972 | 0.7132 |63| No log        | 3.0   | 354  | 0.7146          | 0.6164   | 0.5737    | 0.9027 | 0.7016 | 0.7210 |64| No log        | 4.0   | 472  | 0.7390          | 0.6563   | 0.6356    | 0.7307 | 0.6798 | 0.7132 |65| 0.548         | 5.0   | 590  | 0.9065          | 0.6463   | 0.6275    | 0.7182 | 0.6698 | 0.6949 |66 67 68### Framework versions69 70- Transformers 4.44.171- Pytorch 1.11.072- Datasets 2.12.073- Tokenizers 0.19.174